Now do the same for people who buy their food at the grocery store instead of growing it themselves like our ancestors did.

The worst to me is trying to use someone else's AI code. This guy, for example, has thousands of lines of undocumented (there are a few docstrings but not much more) code. I'm not really motivated to go through that, and you can't trust AI to go through and document it correctly necessarily either. At least if I generated it, I was probably around for enough of the process to know how most of it works.

I agree but if this year the AI can keep up with amateurs, next year who knows?

Claude's research mode is by far the worst one I've used, I consider it nearly useless. I cannot trust it specifically because Anthropic has a policy of refusing to use reddit for anything, whether it be as a research source or in claude chrome.

Reddit may not be the greatest source for hard science, but for things like "tell me what shoes people are finding helpful for their plantar fasciitis" I appreciate reddit's anecdata of reddit over every other source.

There are basically three types of responses you can get from an LLM/agent:

1) A response originating from LLM pre-training, in a domain where there has not been any (successful) Rl-for-reasoning post-training. In this case the amount of reasoning around the raw facts "recalled" by the LLM is going to be limited by any reasoning present in the training data.

2) A non-agentic response in a domain like Math Olmypiad problems where the LLM was post-trained with RL to encourage reasoning mirroring this RL training set. This type of domain-specific reasoning training seems to have little benefit to other domains (although in the early LLM days it was said that training on computer code did provide some general benefit).

3) An agentic response, such as from one of these research systems, where it seems the agent is following some sort of generic research / summarization template with proscribed steps. I've never tried these myself, but it seems they can be quite successful in deep diving and gathering relevant source material, but then the ability to reason over this retrieved material is going to come down to the reasoning capability of the underlying model per 1) and 2) above.

Bottom line would seem to be that with today's systems domain specific reasoning capability largely comes down to RL post-training for reasoning in that specific domain, resulting in what some call "jagged" performance - excellent in some areas and very poor in others. Demis Hassabis, for one, seems to be saying that this will not be fixed until architectural changes/additions are made to bring us closer to AGI.

I don't think he claims to be an "AI researcher". His CV has:

>I built significant pieces of the Copilot onboarding, purchasing, billing and settings flow. For eight months I headed up the Copilot anti-abuse effort. I then led the launch of GitHub Models, and am now working on other Copilot projects.

As an aside I had a look at GitHub Models and it was quite interesting - you can try the API for a number of models for free using your GitHub login.

Nah I think the more obvious thing is that most of the code they’re writing (by sheer volume) is the same stuff most programmers write (code that does telemetry, APIs, usage models, billing, product features, react widgets, etc etc.) stuff that the AI coding models do really well.

And even with pure model development, making incremental changes to try different strategies in notebooks etc are probably not that hard to write, when given clear instructions by a data scientist, etc. (I’m not saying these disciplines are easy, I’m saying that a data scientist could more easily describe what they want)

Or the devops stuff. Or the RL UIs.

All that stuff is run of the mill software in service of building the models. And it can be vibe coded.

> I’ve had the exact same experience. I’ve been vibe coding most of my research now, previously was an MLE handcrafting model code.

What happened to the MLE? Are they all going to end up that way?

> A lot of negative comments on here, which seems to always be the case with HN and vibe coding. The reality is that it’s actually starting to work, quite well.

It's hard to be positive about the idea of your skills getting devalued and getting kicked to the curb.

>The interesting thing is when AI is lifting up the ceiling everywhere, but maybe then is when we are almost on AGI territory.

I feel like that's the only way this insanely high bet on AI could possibly pay off - if AGI/"ceiling lifting" is achieved then maybe all this could be the trillion+ bet they expect. If it doesn't really progress much beyond where it is now, they're in trouble.

We already have a lifted ceiling.

We have several synthetic datasets and automated evaluation options for such things that were close to impossible to do before LLMs.

If one can easily reach parity with a motivated undergrad by leveraging LLMs I will still consider it impressive.

While the 5-minutes model will never be useful in itself it lays the groundwork for amateurs and small groups to getting into developing small models. There's at the moment another HN headline hyping up a tiny model that scores impressively at the arc-agi benchmarks so it's clearly not a dead end to explore what is "household-affordable" models.

Though an approach that doesn't lean on the authors $200/month OAI sub would've been more interesting to follow.